基于频域电磁法反演喀斯特表层土－岩结构研究

程凭; 程勤波; 陈喜; 刘金涛; 张志才; 高满

doi:10.11932/karst20220501

基于频域电磁法反演喀斯特表层土－岩结构研究

doi: 10.11932/karst20220501

程凭^{1, 2,},
程勤波³,
陈喜^{1, 2, ,},
刘金涛³,
张志才³,
高满^{1, 2}

1.
天津大学地球系统科学学院表层地球系统科学研究院, 天津 300072
2.
天津市环渤海关键带科学与可持续发展重点研究室, 天津 300072
3.
河海大学水文水资源学院, 江苏南京 210098

基金项目: 国家自然科学基金项目（42030506, 42071039）

详细信息

作者简介:
程凭（1997－），男，理学硕士，主要研究方向为水文地球物理。E-mail：chengping0221@163.com

通讯作者:
陈喜（1964－），男，教授，主要研究方向为流域水文模拟、地下水数值计算等。E-mail： xi_chen@tju.edu.cn

中图分类号: P631
计量
- 文章访问数: 1761
- HTML浏览量: 1066
- PDF下载量: 52
- 被引次数: 0
出版历程
- 收稿日期: 2021-12-09
- 刊出日期: 2022-12-02

Exploration of superficial soil-rock structure for karst area based on frequency domain electromagnetic method

CHENG Ping^{1, 2
,},
CHENG Qinbo³,
CHEN Xi^{1, 2
, ,},
LIU Jintao³,
ZHANG Zhicai³,
GAO Man^{1, 2}

1.
Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
2.
Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin 300072, China
3.
College of Hydrology and Water Resources, Hohai University, Nanjing,Jiangsu 210098, China

摘要

摘要: 喀斯特地区浅表层土壤分布极不均匀，探测土－岩界面和土壤分布对区域水文以及生态环境研究具有重要意义。文章基于麦克斯韦方程组构建了频域电磁法（FDEM）探测的电导率（EC）一维反演模型，实现了喀斯特浅表剖面EC可视化表述。根据设定的理想地层EC数据以及南方喀斯特峰丛洼地两个剖面和出露的三个实测剖面的FDEM实测视电导率，结合高密度电法、剖面实测土－岩界面，检验了反演模型可靠性。结果表明：FDEM法反演结果能较好的描述理想地层EC变化，以及土壤与灰岩、白云岩界面EC分布，进而可辨识土壤厚度分布，但基于反演的EC值判别尺度较小的溶沟（槽）以及泥岩区土－岩界面误差较大。
- 土壤厚度 /
- 土岩界面 /
- 频域电磁法 /
- 反演算法 /
- 喀斯特地区
Abstract: Soil thickness as a key hydrological and ecological factor, is distributed extremely uneven in karst areas. The explorations of the soil thickness and soil-rock interfaces are still challenge. In this study, a 1-D electrical conductivity (EC) inversion model was developed for the frequency domain electromagnetic method (EMI) based on Maxwell's equation system. The visualization of EC distribution for soil profile in karst area could be realized by the model. The inversion model was validated according to the given ideal stratigraphic EC data and applied to two detection lines and the three exposed profiles at a karst depression. The detected apparent electrical conductivities and soil-rock interfaces from EMI are further compared with those from high-density electrical method. The results showed that the inversion from EMI can capture the variation of ECat ideal strata and interfaces of soil layer and underlying limestone and dolomite bedrock at field. The distribution of soil thickness could be estimated with the detected soil-rock interface. However, there is relatively low accuracy when the model is applied to detect trench (trough) at the small scale or the soil-rock interfaces with mudstone.
- soil thickness /
- soil-rock interface /
- frequency domain electromagnetic method /
- inversion method /
- karst area

HTML

图 1 CMD Explorer频域电磁仪示意图(改自Jadoon et al^[12])

Figure 1. Diagram of CMD Explorer frequency domain electromagnetic induction instrument (modified from Jadoon et al^[12])

下载: 全尺寸图片幻灯片

图 2 频域电磁感应仪工作原理图（改自SELEPENG^[13]）

Figure 2. Working principle diagram of frequency domain electromagnetic induction instrument (FDEM) (modified from SELEPENG^[13])

下载: 全尺寸图片幻灯片

图 3 土壤或岩石分层示意图（改自Deidda et al., 2017^[19]）

Figure 3. Schematic diagram of soil or rock stratification (modified from Deidda et al., 2017^[19])

下载: 全尺寸图片幻灯片

图 4 地层EC反演（虚线）与设定（实线）分布对比图

Figure 4. Comparison of the inversion EC line (dotted line) and the set line (solid line) of the profiles

下载: 全尺寸图片幻灯片

图 5 研究断面位置示意图

Figure 5. Schematic diagram of the location of the study sections

下载: 全尺寸图片幻灯片

图 6 高密度电法（a）与频域电磁法反演结果（b）对比图（Line 1）

Figure 6. Comparison of the electric resistivity distribution measured by ERT (a) and the EC distribution inversed by FDEM at the line 1

下载: 全尺寸图片幻灯片

图 7 高密度电法（a）与频域电磁法反演结果（b）对比图（line 2）

Figure 7. Comparison of the electric resistivity values measured by ERT (a) and the EC values inversed by FDEM at the section of line 2

下载: 全尺寸图片幻灯片

图 8 FDEM反演的视电导率与观测值相关关系

Figure 8. Comparison of the measured and the inverted ECa values

下载: 全尺寸图片幻灯片

图 9 厚层灰岩剖面（a.实景照片，b.反演的EC，c.探测的ECa）

Figure 9. Thick limestone profile (a. real photo, b. inverted EC, c. measured ECa)

下载: 全尺寸图片幻灯片

图 10 黄土-白云岩/泥岩互层剖面（a.实景照片，b.反演的EC，c.探测的EC_a）

Figure 10. Loess-Dolomite/Mudstone interbed profile (a. real photo, b. inverted EC, c. detected EC_a)

下载: 全尺寸图片幻灯片

图 11 黄土－白云岩/泥灰岩剖面（a.实景照片，b.反演的EC，c.探测的EC_a）

Figure 11. Loess-Dolomite/Marl profile (a. real photo, b. inverted EC, c. detected EC_a)

下载: 全尺寸图片幻灯片

图 12 各剖面反演与实测EC_a对比图

Figure 12. Comparison of inverted and measured EC_a of each profile

下载: 全尺寸图片幻灯片

表 1 正演模型模拟与反演ECa结果对比

Table 1. Comparison of the simulated and inverted ECa values

	线圈模式	HCP			VCP			残差
	线圈距离/m	1.48	2.82	4.49	1.48	2.82	4.49
a	模拟 ECa/S·m⁻¹	0.059 2	0.051 8	0.043 8	0.059 3	0.057 5	0.053 8	0.000 2
	反演 ECa/S·m⁻¹	0.058 8	0.051 7	0.0441	0.059 5	0.057 5	0.053 9	0.000 2
b	模拟 ECa/S·m⁻¹	0.064 1	0.055 9	0.048 6	0.071 5	0.065 9	0.060 7	0.000 3
	反演 ECa/S·m⁻¹	0.064 1	0.055 8	0.048 7	0.071 5	0.065 8	0.060 7	0.000 3
c	模拟 ECa/S·m⁻¹	0.064 1	0.055 9	0.0486	0.0715	0.065 9	0.060 7	0.000 2
	反演 ECa/S·m⁻¹	0.064 1	0.055 8	0.048 7	0.071 5	0.065 8	0.060 7	0.000 2
d	模拟 ECa/S·m⁻¹	0.076 9	0.082 8	0.077 4	0.061 2	0.070 7	0.074 4	0.000 0
	反演 ECa/S·m⁻¹	0.076 7	0.083 1	0.077 2	0.061 2	0.070 8	0.074 5	0.000 0

下载: 导出CSV

参考文献(21)

[1]	Ford D, Williams P D. Karst hydrogeology and geomorphology[M]. New York: John Wiley & Sons, 2013.
[2]	陈喜. 西南喀斯特地区水循环过程及其水文生态效应[M]. 北京: 科学出版社, 2014. CHEN Xi. Water cycle processes and hydro-ecological effects in the southwest karst region[M]. Beijing: Science Press, 2014.
[3]	王甲荣, 陈喜, 张志才, 张润润, 朱彪, 龚轶芳, 刘皓, 袁瞬飞. 喀斯特溶槽岩-土界面优势流及其对土壤水分动态的影响[J]. 中国岩溶, 2019, 38(1):109-116. WANG Jiarong, CHEN Xi, ZHANG Zhicai, ZHANG Runrun, ZHU Biao, GONG Yifang, LIU Hao, YUAN Shunfei. Preference flow at rock-soil interface and its influence on soil water dynamics in the karst troughs[J]. Carsologica Sinica, 2019, 38(1):109-116.
[4]	Archie, G. E. The electrical resistivity log as an aid in determining some reservoir characteristics[J]. Transactions of the Aime, 1942, 146(1):54-62. doi: 10.2118/942054-G
[5]	Revil A, Cathles L M, Losh S, Nunn J.A Electrical conductivity in shaly sands with geophysical applications[J]. Journal of Geophysical Research Solid Earth, 1998, 103(B10):23925-23936. doi: 10.1029/98JB02125
[6]	Corwin D L , Lesch S M , Shouse P J , Soppe R, Ayars J E. Identifing soil properties that influence cotton yield using soil sampling directed by apparent bulk soil electrical conductivity[J]. Agronomy Journal, 2003, 95(2).
[7]	Zhang Z, Routh P S, Oldenburg D W, Alumbaugh D L, Newman G A. Reconstruction of 1-D conductivity from dual-loop EM data[J]. Geophysics, 2000, 65(2):492-501. doi: 10.1190/1.1444743
[8]	Moghadas D, Jadoon K Z, Mccabe M F. Spatiotemporal monitoring of soil moisture from EMI data using DCT-based Bayesian inference and neural network[J]. Journal of Applied Geophysics, 2019, 169:226-238. doi: 10.1016/j.jappgeo.2019.07.004
[9]	Akramkhanov A , Brus D J, Walvoort D J J. Geostatistical monitoring of soil salinity in Uzbekistan by repeated \\{EMI\\ surveys[J]. Geoderma, 2014, 213: 600-607.
[10]	Cheng Qinbo, Tao Min, Chen Xi, Binley A. Evaluation of electrical resistivity tomography (ERT) for mapping the soil–rock interface in karstic environments[J]. Environmental Earth Sciences, 2019, 78(15):1-14.
[11]	Cheng Qinbo, Chen Xi, Tao Min, Binley A. Characterization of karst structures using quasi-3D electrical resistivity tomography[J]. Environmental Earth Sciences, 2019, 78(9):1-12.
[12]	Jadoon K Z, Moghadas D, Jadoon A, Missimer T M, Al-Mashharawi S K, McCabe M F. Estimation of soil salinity in a drip irrigation system by using joint inversion of multicoil electromagnetic induction measurements[J]. Water Resources Research, 2015, 51 (5):3490-3504.
[13]	Selepeng A T. Three Dimensional Numerical Modeling of Loop-Loop Electromagnetic Data at Low Induction Numbers[J]. 2016.
[14]	Santos F. 1-D laterally constrained inversion of EM34 profiling data[J]. Journal of Applied Geophysics, 2004, 56(2):123-134. doi: 10.1016/j.jappgeo.2004.04.005
[15]	Dafflon B, Hubbard S S, Ulrich C, Peterson J E. Electrical Conductivity Imaging of Active Layer and Permafrost in an Arctic Ecosystem, through Advanced Inversion of Electromagnetic Induction Data[J]. Vadose Zone Journal, 2013, 12(4):1742-1751.
[16]	Moghadas D, Jadoon K Z, Mccabe M F. Spatiotemporal monitoring of soil water content profiles in an irrigated field using probabilistic inversion of time-lapse EMI data[J]. Advances in Water Resources, 2017, 110:238-248.
[17]	Mclachlan P, Blanchy G, Binley A. EMagPy: open-source standalone software for processing, forward modeling and inversion of electromagnetic induction data[J]. Computers & Geosciences, 2020:104561.
[18]	Wait J. Geo-electromagnetism[M]. Amsterdam: Elsevier, 2012.
[19]	Deidda G P, Diaz De Alba P, Rodriguez G. Identifying the magnetic permeability in multi-frequency EM data inversion[J]. 2017.
[20]	Chalikakis K, Plagnes V, Guerin R, Valois R, Bosch F. Contribution of geophysical methods to karst-system exploration: an overview[J]. Hydrogeology Journal, 2011, 19(6):1169-1180. doi: 10.1007/s10040-011-0746-x
[21]	Loke M H, Chambers J E, Rucker D F, Kuras O, Wilkinson P B. Recent developments in the direct-current geoelectrical imaging method[J]. Journal of Applied Geophysics, 2013, 95(8): 135-156.